Step 1 in ROP399 – What’s my project?

This week I finally decided on my project topic!

During last week’s lab meeting, Dr. Tyrrell brought up some potential topics for us to choose from. This included determining the appropriate sample size for machine learning, class imbalance problem, participating in the dental project and the ultrasound project that has just been brought up.

After the lab meeting, I talked to Wenda and Ariana regarding the dental project that they have been working on. This was the project that I wanted to be in the most primarily because I intend to go to dental school after graduation, and being involved in a dental project would offer more exposure to this field. However, after the brief introduction and update on the current progress by Wenda and Ariana, I realized that there might not be much to do as a complete project. Hanatu, an independent research student, would also be working on this project, leaving fewer gaps that need to be addressed for the project. Because my expectation is to work on a project independently on a topic where there’s plenty of freedom, I decided to change gears and look at other ideas.

The class imbalance topic was the next thing that caught my interest. Indranil, who happened to be my mentor before I joined the lab, has been working on the class imbalance project before. I immediately contacted him regarding this project and got his project report. I was told that this topic is more technical and less clinical than the dental project, so I didn’t know if I would like the topic. Surprisingly, I found it really interesting and has great implications. Indranil studied the effect of class imbalance using images in the IRMA database and applied the random forest model. By manually changing the sample size of one class, he found that as the proportion of the imbalanced set goes up, the overall accuracy of the model decreases, while the accuracy for the imbalanced class increases. I found it interesting and useful, as class imbalance can be very common in any dataset, especially in medical imaging. Studying its effect can help identify this issue when machine learning is applied to assist with medical imaging.

I then met with Indranil on the possible projects on this topic, and the most natural one would just be investigating which method can better mitigate the class imbalance problem – as a continuation after studying its effects. Next, I researched on any existing literature on this topic specifically in medical imaging, and very little was found. The most commonly used methods for class imbalance include over-sampling, under-sampling, and changing the weight for the imbalanced class coefficient in the cost function. I met with Dr. Tyrrell, he liked the idea for my project, and suggested that I focus on these 3 main methods (mentioned above).

I am excited about my project (and most importantly, really interested). I decide to ask for the code that Indranil used to do the image preprocessing and creating imbalanced classes as a starting point. For my next steps, I’m also planning to learn more about the different methods in addressing this problem as well as how to code in Python.

Looking forward to working on my project!

Wendi
Sep.28, 2018

Upcoming Medical Imaging – Artificial Intelligence Workshop in Calgary!

MiDATA will be offering a free Mi-AI workshop on Tuesday December 11th from 8:30 am-12:00 noon at the Alberta Children’s Hospital, Calgary.

The focus will be on introducing participants to the concepts of AI, deep learning, and machine learning in medical imaging research.

Workshop objectives will include answering the following questions:
-How to design a research question for AI?
-I have an idea, a laptop, and a few scans. Am I good to go?
-What do I need to get started?
-What is currently being used like AI techniques for medical applications?

Indranil Balki Receives Undergraduate Research Fund Prize

Recently from our centre, Indranil Balki, under the supervision of Dr. Pascal Tyrrell, received the Undergraduate Research Fund Prize, a prestigious, semi-annual award presented for innovative research at the University of Toronto. The grant has helped to fund the purchase of a Graphics Processing Unit (GPU) at the Data Science unit in the Department of Medical Imaging. The GPU will add versatility and flexibility to the machine learning tools available for students and staff at the lab – supporting projects that leverage AI in medical image analysis and aid in the investigation of broader issues ranging from class imbalance to sample size determination in machine learning.

Indranil is enrolled in medical school at the University of Toronto and recently completed his undergraduate degree in Statistics & Biology. His research experiences in Prof. Tyrrell’s units inspire Indranil to leverage data science, including machine learning, database management and cost-effectiveness analysis to improve clinical care.

New GPU

Woohoo!! The new GPU in our lab is up and running!

Here’s the specs!
CPU: Intel i7 8th gen, 6-core 12-thread
RAM: 32Gb DDR4 3400 MHz, upgradeable to 64Gb
Storage: 500Gb M2 SSD, 6TB internal HDD
GPU: 2 NVIDIA GeForce GTX 1080Ti 11Gb
OS: Ubuntu 16.04

“Hour of Code… Part Deux” at the University of Toronto

What hour of code? What code?

The Hour of Code is a global movement reaching tens of millions of students in 180+ countries. The purpose is to to demystify “code”, to show that anybody can learn the basics, and to broaden participation in the field of computer science. Please see here for more info.

I belong to Code.org a non-profit dedicated to expanding access to computer science, and increasing participation by women and underrepresented students of color.

On Thursday, December 7th, 2017 at 9:30AM we will be hosting our second 
 
MiDATA Hour of Code at UofT

What is the purpose of this event?

To engage young minds and help them see the exciting possibilities computer programming can offer them in their future careers.

Who is coming?

Over 100 students (grades 7 to 11) from Central Toronto Academy (TDSB), St Francis Assisi and St Ignatius of Loyola (TCDSB).

Who will be engaging them (so far)?

University of Toronto:
MiDATA (Data Science unit from the Department of Medical Imaging)
*Prof Pascal Tyrrell – Director, Data Science

*Prof Anne Martel – Medical Biophysics (Machine Learning)

*Daniel Eftekhari – MSc student (medical image machine learning)

*Dr Mariam Afshin – Research Physicist

*Rasha Mahmood – VBIRG

Department of Medical Imaging
*Dr Alan Moody – Radiologist and Chair of the Department

Department of Statistical Sciences
*Prof Paul Corey – Senior Biostatistician
*Chris Meaney – Biostatistician

Department of Computer Science
*Prof Steve Engels

 

Industry:
IBM Watson Health and Merge Healthcare

   *George Gorthy – Senior Sales Consultant IBM Watson Health Imaging
    *Marwan Sati – VP of Development, Clinical Speciality Solutions, Merge Healthcare
*Aditya  Sriram – Developer, IBM Watson Health Imaging

Microsoft (Big Data and Analytics)
* Sage Franch – Microsoft Canada

SAS Canada
*Mark Morreale – Lead, Academic Program

Community:
Ladies Learning Code – Yaa Otchere

 

Industry Support:
Google

Tyrrell lab students and Computer Science undergraduates will be acting as ambassadors.

Where and when will the event be held…exactly?

University College Media Room (RM 140 and RM148) from 9:30 AM to 1 PM

University College, University of Toronto
15 King’s College Circle
Toronto, Ontario

 

Interested in participating? Contact me at pascal.tyrrell@utoronto.ca!

See you all there,

Pascal Tyrrell

MiDATA hosts “Hour of Code” at the University of Toronto

What hour of code? What code?

The Hour of Code is a global movement reaching tens of millions of students in 180+ countries. The purpose is to to demystify “code”, to show that anybody can learn the basics, and to broaden participation in the field of computer science. Please see here for more info.

I belong to Code.org a non-profit dedicated to expanding access to computer science, and increasing participation by women and underrepresented students of color.

On Wednesday, December 7th at 10AM we will be hosting the inaugural

“MiDATA Hour of Code at UofT”

What is the purpose of this event?

To engage young minds and help them see the exciting possibilities computer programming can offer them in their future careers.

Who is coming?

Over 100 students (grades 7 to 11) from Central Toronto Academy (TDSB), St Francis Assisi and St Ignatius of Loyola (TCDSB).

Who will be engaging them (so far)?

University of Toronto:
MiDATA (Data Science unit from the Department of Medical Imaging)
*Prof Pascal Tyrrell – Director, Data Science

*Prof Anne Martel – Medical Biophysics (Machine Learning)

*John Harvey – Information Architect

MiNE (Medical image Network Enterprise, Sunnybrook Health Science Centre)
*Dr Mariam Afshin – Research Physicist
*Rasha Mahmood – VBIRG

Department of Medical Imaging
*Dr Alan Moody – Radiologist and Chair of the Department

Department of Statistical Sciences
*Prof Jamie Stafford – Statistician and Chair of the Department
*Prof Paul Corey – Senior Biostatistician

Translational Research Program /Institute of Medical Science
*Prof Joseph Ferenbok – Program Director

Department of Computer Science
*Prof Francois Pitt

Faculty of Engineering
*Prof Naomi Matsuura – Department of Materials Science & Engineering

Industry:
IBM Watson Health and Merge Healthcare
*Steve Schudlo – Executive Director, Strategic Alliances, IBM Watson Health Imaging
*Marwan Sati – VP of Development, Clinical Speciality Solutions, Merge Healthcare
*Aditya Sriram – Developer, Watson Health Imaging, IBM

Microsoft (Big Data and Analytics)
*Mark Godfrey – Cloud Architect, TSP – Cloud & Data Center, Microsoft Canada

AceAge
*Spencer Waugh – CEO AceAge
*Sam Campbell – CTO AceAge
*Dylan Horvath – Cortex Design President

SAS Canada
*Mark Morreale – Lead, Academic Program

Community:
Ladies Learning Code – Yaa Otchere

Tyrrell lab students and Computer Science undergraduates will be acting as ambassadors.

Where and when will the event be held…exactly?

University College Media Room (RM 140 and RM148) from 10 AM to 1 PM

University College, University of Toronto
15 King’s College Circle
Toronto, Ontario

 

Interested in participating? Contact me at pascal.tyrrell@utoronto.ca!

See you all there,

Pascal Tyrrell

Engaging Primary Care in Research: Not Always an Easy Task

I am Stella Bing Xin Song, currently a second year student studying pharmacology and psychology at University of Toronto. I was fortunate to be a part of the 2016 Research Opportunity Program (ROP) supervised by Dr. Pascal Tyrrell in the Department of Medical Imaging at University of Toronto. 
My ROP project focused on evaluating the feasibility of using MRI as the primary imaging modality for carotid artery stenosis diagnosis and assessment (not sure what we are talking about? See previous posts here and here). Along with Ginni Ting, a student volunteer in Dr. Tyrrell’s lab, we surveyed physicians in the Niagara region of Ontario to learn about their perspectives on this proposal. Our community partner in this research was Heart Niagara – a fantastic local organization that has been guiding advances in cardiac health education and services since 1977.
Most of the responding physicians saw approximately 2000 or more patients per year. Physicians expressed a variety of care-related decisions for carotid artery stenosis patients, especially for those where diagnosis was less obvious with less than 70% stenosis. Most responding physicians would consider MRI over Ultrasound as the first-line diagnostic imaging modality, because of its ability to detect IPH yielding more pertinent information. IPH is bleeding within the plaques, which causes them to become more vulnerable (see vulnerable plaque). There is a 6 times greater risk of stroke in people with IPH! For those who were reluctant to consider it, they expressed that it was mostly due to their concerns for the relative cost and current wait time for MRI. 

Unfortunately, the response rate for this online survey was very low. Reasons given for the reluctance to participate were that physicians were on a tight schedule and were busy with their patients. Feedback from participants was that the online survey seemed long. Nevertheless, from the responses received, we were able to learn more about physicians’ perspectives of using MRI for carotid artery stenosis diagnosis and assessment.

In the end, it was an exciting and valuable experience to plan out and execute this research project. Most importantly, I had the pleasure to join Dr. Tyrrell’s lab and meet his team. I am grateful for all the help and support which I have received throughout my time at the lab. I look forward to continuing to work as a member of Dr. Tyrrell’s lab.

Stella Bing